Perceiving the Ukraine-Russia Conflict: Topic Modeling and Clustering on Twitter Data

This study aims to gain insight into public perception of the ongoing Russia-Ukraine conflict by analyzing tweets collected since March 2022. Text-mining techniques, including Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Non-Negative Matrix Factorization (NMF), are used to analyze high-frequency words in tweets and identify patterns. Additionally, clustering techniques such as K-means and Hierarchical-Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) are applied to dense tweet embeddings to create semantic topical groupings, which we compare to traditional topic modeling approaches using a coherence metric. We demonstrate the effectiveness of the proposed methodology by identifying the commonly used terms, the meaningful topics, and the most discussed topic among tweets related to the Russia-Ukraine conflict to help gain a deeper understanding of public discourse on the conflict.